Data and package
library(scran)
library(SingleCellExperiment)
library(scater)
library(scattermore)
library(moon)
library(ggplot2)
library(ggthemes)
library(ggpubr)
library(reshape2)
library(dplyr)
library(stringr)
library(pheatmap)
library(CellChat)
library(cluster)
library(RColorBrewer)
library(gridExtra)
library(scales)
meta_chua <- readRDS("results/chua_results/meta_data_chua.rds")
df_toPlot_colData <- readRDS("results/chua_results/df_toPlot_chua_final.rds")
cellType_names <- names(table(df_toPlot_colData$scClassify_cluster))
cellType_color <- tableau_color_pal("Tableau 20")(20)[c(19, 6, 3, 5, 15, 11, 9, 2, 17, 18, 7, 10, 1)]
cellType_color <- c(cellType_color, "grey80", "grey50")
severity_color <- c("#2ca02c", "#FFD92F", "#7570B3")
names(severity_color) <- c("control", "moderate", "critical")
names(cellType_color) <- c(cellType_names[!cellType_names %in% c("intermediate", "unassigned")], "intermediate", "unassigned")
df_toPlot_colData$scClassify_cluster <- factor(df_toPlot_colData$scClassify_cluster,
levels = names(cellType_color[c(6, 3, 11, 2, 4, 7, 12, 8, 13, 10, 5, 9, 1, 14, 15)]))
stage_color <- RColorBrewer::brewer.pal(12, "Paired")[c(2, 7, 1, 10, 12, 9)]
names(stage_color) <- levels(factor(meta_chua$stage))
stage_color <- stage_color[c(1, 2, 3, 5, 6, 4)]
anno_col <- data.frame(severity = meta_chua$severity,
stage = meta_chua$stage)
rownames(anno_col) <- meta_chua$sample
anno_color <- list(severity = severity_color,
stage = stage_color)
epi_cellType <- c("Basal", "Ciliated", "Goblet", "Ionocyte", "Squamous")
immune_cellType <- c("B", "Dendritic", "Macrophage",
"Monocyte", "Neutrophil", "T")
# CCI results (Cellchat)
cellchat_res_list <- readRDS("results/chua_results/chua_cellchat_res_list.rds")
Overall pattern analysis
rankNet_byCellType <- function(object, slot.name = "netP",
x.rotation = 90, title = NULL, color.use = NULL,
bar.w = 0.75, font.size = 8)
{
object1 <- methods::slot(object, slot.name)
prob1 = object1$prob
df <- melt(apply(prob1, 3, function(x) {
df <- melt(x)
colnames(df) <- c("Ligand", "Receptor", "value")
df
}))
df <- df[, c("Ligand", "Receptor", "L1", "value")]
colnames(df)[3] <- "Pathway"
return(df)
}
rankNet_byCellType_list <- lapply(cellchat_res_list, rankNet_byCellType)
rankNet_byCellType_list <- melt(rankNet_byCellType_list)
rankNet_byCellType_list$Ligand_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Ligand), "_"), "[[", 1))
rankNet_byCellType_list$Receptor_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Receptor), "_"), "[[", 1))
#saveRDS(rankNet_byCellType_list, file = "results/chua_results/rankNet_byCellType_list_chua.rds")
rankNet_byGroup_agg <- aggregate(rankNet_byCellType_list$value,
list(rankNet_byCellType_list$Ligand_group,
rankNet_byCellType_list$Receptor_group,
rankNet_byCellType_list$L1,
rankNet_byCellType_list$Pathway),
sum)
colnames(rankNet_byGroup_agg) <- c("Ligand_group",
"Receptor_group",
"sample",
"Pathway",
"value")
features <- paste(rankNet_byGroup_agg$Ligand_group,
rankNet_byGroup_agg$Receptor_group,
rankNet_byGroup_agg$Pathway, sep = "_")
rankNet_byGroup_agg$features <- features
rankNet_byGroup_agg_all <- dcast2(rankNet_byGroup_agg,
features ~ sample,
fun.aggregate = sum, value.var = "value")
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all) > 0, ]
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all!=0) > 2, ]
Feature selction: kruskal test
kruskal_pvalue <- list()
for (i in 1:nrow(rankNet_byGroup_agg_all)) {
if (i %% 100 == 0) cat(i, "...")
kruskal_res <- try(kruskal.test(unlist(rankNet_byGroup_agg_all[i,]) ~ meta_chua[colnames(rankNet_byGroup_agg_all), ]$severity), silent = TRUE)
kruskal_pvalue[[i]] <- try(kruskal_res$p.value, silent = TRUE)
}
kruskal_pvalue <- lapply(kruskal_pvalue, function(x) {
if (class(x) == "try-error") {
x <- NULL
}
x
})
names(kruskal_pvalue) <- rownames(rankNet_byGroup_agg_all)
kruskal_pvalue <- unlist(kruskal_pvalue)
kruskal_pvalue <- p.adjust(kruskal_pvalue, method = "BH")
sort(kruskal_pvalue)[1:20]
saveRDS(kruskal_pvalue, "results/chua_results/CCI_kruskal_pvalue_condition_chua.rds")
PCA
pca_patient <- prcomp(t(-1/log(rankNet_byGroup_agg_all[names(sort(kruskal_pvalue))[1:2000],])),
scale. = TRUE, center = TRUE)
epi_immune <- expand.grid(epi_cellType, immune_cellType)
epi_immune <- paste(epi_immune[, 1], epi_immune[, 2], sep = "_")
epi_immune_idx <- grep(paste(epi_immune, collapse = "|"), names(kruskal_pvalue))
pca_patient_epi <- prcomp(t(-1/log(rankNet_byGroup_agg_all[names(sort(kruskal_pvalue[epi_immune_idx])[1:500]),])),
scale. = TRUE, center = TRUE)
library(ggrepel)
pca1 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 2],
color = meta_chua[rownames(pca_patient$x),]$severity)) +
geom_point(size = 4, alpha = 0.8) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA2") +
labs(color = "")
pca2 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 3],
color = meta_chua[rownames(pca_patient$x),]$severity)) +
geom_point(size = 3, alpha = 0.8) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA3") +
labs(color = "")
pca3 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 2],
y = pca_patient$x[, 3],
color = meta_chua[rownames(pca_patient$x),]$severity)) +
geom_point(size = 3, alpha = 0.8) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA2") +
ylab("PCA3") +
labs(color = "")
ggarrange(pca1, pca2, pca3, align = "hv",
common.legend = TRUE, ncol = 3, nrow = 1)
pca1
ggsaveWithDate("figures/ChuaEtAl/PCA_patients_bySeverity", width = 6, height = 5)
df_pca <- data.frame(variance_explained = pca_patient$sdev^2/sum(pca_patient$sdev^2),
nPCs = 1:32)
ggplot(df_pca, aes(x = nPCs, y = variance_explained)) +
geom_point(alpha = 0.8, size = 2) +
theme_yx() +
theme(aspect.ratio = 1) +
ylab("% variance expalined")
ggsaveWithDate("figures/ChuaEtAl/PCA_variance_expalined", width = 6, height = 5)
Aggregation by samples
aff_mat_bySample <- lapply(split(rankNet_byGroup_agg, rankNet_byGroup_agg$sample),
function(x) dcast2(x, Ligand_group~Receptor_group,
fun.aggregate = sum, value.var = "value"))
all_cellTypes <- names(table(rankNet_byGroup_agg$Ligand_group))
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
mat <- matrix(0, ncol = length(all_cellTypes), nrow = length(all_cellTypes))
colnames(mat) <- rownames(mat) <- all_cellTypes
mat[rownames(x), colnames(x)] <- as.matrix(x)
mat
})
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
(x - min(x))/(max(x) - min(x))
})
p <- lapply(1:length(aff_mat_bySample), function(i) {
pheatmap(aff_mat_bySample[[i]],
cluster_cols = FALSE,
cluster_rows = FALSE,
main = names(aff_mat_bySample)[i],
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100))
})
pdfWithDate("figures/ChuaEtAl/cellchat_CCI_network_sample_byCellType",
width = 20, height = 16)
do.call(grid.arrange, list(grobs = lapply(p, function(x) x$gtable), ncol = 6))
dev.off()
## quartz_off_screen
## 2
Aggregation by conditions
severe_patients <- rownames(meta_chua)[meta_chua$severity == "critical"]
aff_mat_severe <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% severe_patients])/length(severe_patients)
moderate_patients <- rownames(meta_chua)[meta_chua$severity == "moderate"]
aff_mat_moderate <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% moderate_patients])/length(moderate_patients)
control_patients <- rownames(meta_chua)[meta_chua$severity == "control"]
aff_mat_control <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% control_patients])/length(control_patients)
p_severe <- pheatmap(aff_mat_severe, cluster_cols = FALSE,
cluster_rows = FALSE,
main = "severe (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_moderate), max(aff_mat_moderate)/100))
library(RColorBrewer)
p_moderate <- pheatmap(aff_mat_moderate,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "moderate (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_moderate), max(aff_mat_moderate)/100))
p_control <- pheatmap(aff_mat_control,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "control (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_control), max(aff_mat_control)/100))
pdfWithDate("figures/ChuaEtAl/cellchat_CCI_network_byCondition",
width = 12, height = 4)
do.call(grid.arrange, list(grobs = list(p_control$gtable,
p_moderate$gtable,
p_severe$gtable), ncol = 3))
dev.off()
## quartz_off_screen
## 2
aff_mat_diff <- aff_mat_severe - aff_mat_moderate
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "server - moderate",
#file = "figures/ChuaEtAl/cellchat_CCI_network_byCondition_diff_new.pdf",
width = 8,
height = 7)
aff_mat_diff <- aff_mat_moderate - aff_mat_control
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "moderate - control",
#file = "figures/ChuaEtAl/cellchat_CCI_network_byCondition_diff_moderate_control_new.pdf",
width = 8,
height = 7)
aff_mat_diff <- aff_mat_severe - aff_mat_control
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "severe - control",
#file = "figures/ChuaEtAl/cellchat_CCI_network_byCondition_diff_severe_control_new.pdf",
width = 8,
height = 7)
mat <- aff_mat_severe - aff_mat_moderate
cci_severe <- melt(as.matrix(mat))
colnames(cci_severe) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_severe))
E(g)$weights <- ifelse(cci_severe$n == 0,
NA, abs(cci_severe$n))
E(g)$sign <- ifelse(sign(cci_severe$n) == 1, "#d62728", "#1f77b4")
V(g)$color <- cellType_color[V(g)$name]
pdfWithDate("figures/ChuaEtAl/cellchat_CCI_network_byCondition_diff_network.pdf",
width = 8,
height = 6)
plot(g,
edge.arrow.size = 0.5,
vertex.size = 30,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 50,
edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "Severe vs Moderate")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [19] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [37] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [55] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [73] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [91] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [109] 0.3 0.3
dev.off()
## quartz_off_screen
## 2
mat <- aff_mat_moderate - aff_mat_control
cci_severe <- melt(as.matrix(mat))
colnames(cci_severe) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_severe))
E(g)$weights <- ifelse(cci_severe$n == 0,
NA, abs(cci_severe$n))
E(g)$sign <- ifelse(sign(cci_severe$n) == 1, "#d62728", "#1f77b4")
V(g)$color <- cellType_color[V(g)$name]
pdfWithDate("figures/ChuaEtAl/cellchat_CCI_network_byCondition_diff_network_moderate_vs_control.pdf",
width = 8,
height = 6)
plot(g,
edge.arrow.size = 0.5,
vertex.size = 30,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 30,
edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "Moderate vs Control")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [19] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [37] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [55] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [73] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [91] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [109] 0.3 0.3
dev.off()
## quartz_off_screen
## 2
mat <- aff_mat_severe
cci_severe <- melt(as.matrix(mat))
colnames(cci_severe) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
cci_severe <- data.frame(cci_severe)
cci_severe <- cci_severe[cci_severe$n != 0, ]
g <- graph_from_data_frame(cci_severe,
vertices = data.frame(name = levels(cci_severe$Ligand)))
E(g)$weight <- cci_severe$n
V(g)$color <- cellType_color[V(g)$name]
minMax <- function(x) {
(x - min(x))/(max(x) - min(x))
}
pdfWithDate("figures/ChuaEtAl/cellchat_CCI_network_byCondition_Severe_network.pdf",
width = 8,
height = 6)
plot(g,
edge.arrow.size = 0.5,
vertex.size = 30,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weight * 20,
edge.curved = rep(0.2, length(E(g))),
layout = layout_in_circle,
main = "Severe")
## [1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [20] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [39] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [58] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [77] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
dev.off()
## quartz_off_screen
## 2
mat <- aff_mat_moderate
cci_moderate <- melt(as.matrix(mat))
colnames(cci_moderate) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
cci_moderate <- data.frame(cci_moderate)
cci_moderate <- cci_moderate[cci_moderate$n != 0, ]
g <- graph_from_data_frame(cci_moderate,
vertices = data.frame(name = levels(cci_moderate$Ligand)))
E(g)$weight <- cci_moderate$n
V(g)$color <- cellType_color[V(g)$name]
minMax <- function(x) {
(x - min(x))/(max(x) - min(x))
}
pdfWithDate("figures/ChuaEtAl/cellchat_CCI_network_byCondition_moderate_network.pdf",
width = 8,
height = 6)
plot(g,
edge.arrow.size = 0.5,
vertex.size = 30,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weight * 20,
edge.curved = rep(0.2, length(E(g))),
layout = layout_in_circle,
main = "Moderate")
## [1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [19] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [37] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [55] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [73] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [91] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [109] 0.2 0.2
dev.off()
## quartz_off_screen
## 2
mat <- aff_mat_control
cci_control <- melt(as.matrix(mat))
colnames(cci_control) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
cci_control <- data.frame(cci_control)
cci_control <- cci_control[cci_control$n != 0, ]
g <- graph_from_data_frame(cci_control,
vertices = data.frame(name = levels(cci_control$Ligand)))
E(g)$weight <- cci_control$n
V(g)$color <- cellType_color[V(g)$name]
minMax <- function(x) {
(x - min(x))/(max(x) - min(x))
}
pdfWithDate("figures/ChuaEtAl/cellchat_CCI_network_byCondition_control_network.pdf",
width = 8,
height = 6)
plot(g,
edge.arrow.size = 0.5,
vertex.size = 30,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weight * 20,
edge.curved = rep(0.2, length(E(g))),
layout = layout_in_circle,
main = "control")
## [1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
## [20] 0.2 0.2 0.2 0.2 0.2 0.2 0.2
dev.off()
## quartz_off_screen
## 2
Pathway-cluster cell-cell interaction
anno_col <- data.frame(severity = meta_chua$severity,
stage = meta_chua$stage)
rownames(anno_col) <- meta_chua$sample
anno_color <- list(severity = severity_color,
stage = stage_color)
names(anno_color$stage) <- levels(factor(anno_col$stage))
names(anno_color$severity) <- levels(factor(anno_col$severity))
Monocyte -> Neutrophil
keep_monocyte <- rankNet_byCellType_list$Receptor_group %in% "Neutrophil" &
rankNet_byCellType_list$Ligand_group %in% "Monocyte"
pmat_monocytes_neutrophil <- rankNet_byCellType_list[keep_monocyte, ] %>%
dcast2(Pathway~L1,
fun.aggregate = sum, value.var = "value")
pmat_monocytes_neutrophil <- pmat_monocytes_neutrophil[rowSums(pmat_monocytes_neutrophil) != 0 &
rowSums(pmat_monocytes_neutrophil != 0) > 2, ]
hclust_pathway <- hclust(dist((-1/log(pmat_monocytes_neutrophil))),
method = "ward.D2")
nclust_pathway = 6
pathway_clust <- cutree(hclust_pathway, k = nclust_pathway)
saveRDS(pathway_clust, file = "results/chua_results/cellchat_LigandMonocyte_ReceptorNeutrophils_pathway_cluster.rds")
anno_row <- data.frame(pathway_cluster = factor(pathway_clust))
rownames(anno_row) <- names(pathway_clust)
anno_color$pathway_cluster <- RColorBrewer::brewer.pal(nclust_pathway, "Set2")
names(anno_color$pathway_cluster) <- seq_len(nclust_pathway)
pheatmap(-1/log(pmat_monocytes_neutrophil),
annotation_col = anno_col[, 1, drop = FALSE],
annotation_colors = anno_color,
annotation_row = anno_row,
clustering_method = "ward.D2",
color = colorRampPalette(c("white",
brewer.pal(n = 9, name = "Reds")))(100),
main = "Ligand: Monocytes; Recetpor: Neutrophils",
#file = "figures/ChuaEtAl/cellchat_LigandMonocyte_ReceptorNeutrophils_heatmap.pdf",
height = 12,
width = 10
)
dev.off()
## null device
## 1
subset <- rankNet_byCellType_list[keep_monocyte, ] %>%
filter(Pathway %in% rownames(pmat_monocytes_neutrophil))
subset <- aggregate(subset$value, list(subset$Ligand, subset$L1, subset$Pathway), sum)
colnames(subset) <- c("Ligand", "sample", "Pathway", "value")
subset <- merge(subset, meta_chua, by = "sample")
pmat_ligand_critical <- subset %>% filter(severity == "critical") %>%
dcast2(Ligand~Pathway,
fun.aggregate = mean, value.var = "value")
# pmat_ligand_critical <- pmat_ligand_critical[, colSums(pmat_ligand_critical) > 1e-5]
pmat_ligand_moderate <- subset %>% filter(severity == "moderate") %>%
dcast2(Ligand~Pathway,
fun.aggregate = mean, value.var = "value")
# pmat_ligand_moderate <- pmat_ligand_moderate[, colSums(pmat_ligand_moderate) > 1e-5]
for (i in c(2, 3, 4)) {
df_toPlot <- aggregate(subset$value, list(subset$Ligand, subset$Pathway, subset$severity), mean)
colnames(df_toPlot) <- c("Ligand","Pathway", "severity", "value")
df_toPlot$Pathway_cluster <- factor(pathway_clust[df_toPlot$Pathway])
df_toPlot <- df_toPlot %>% filter(
Pathway %in% names(pathway_clust[pathway_clust %in% i])
)
df_toPlot$value[df_toPlot$value == 0] <- NA
df_toPlot$Ligand <- factor(as.character(df_toPlot$Ligand), levels = sort(unique(as.character(df_toPlot$Ligand))))
ggplot(df_toPlot, aes(x = Ligand, y = Pathway,
color = value, size = value)) +
geom_point() +
scale_color_viridis_c() +
theme_yx() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
facet_grid(Pathway_cluster~severity, scales = "free_y", space = "free") +
ylab("") +
ggtitle("Ligand: Monocyte; Receptor: Neutrophil")
if (sum(pathway_clust %in% i) > 10) {
fig_height = 6
} else {
fig_height = 3
}
ggsaveWithDate(paste0("figures/ChuaEtAl/cellchat_LigandMonocyte_ReceptorNeutrophils_dot_pathClust",
i), width = 6, height = fig_height)
}
Goblet -> Immune
keep_Goblet <- rankNet_byCellType_list$Receptor_group %in% c("Macrophage", "Monocyte", "T") &
rankNet_byCellType_list$Ligand_group %in% "Goblet"
pmat_Goblets_Macrophage <- rankNet_byCellType_list[keep_Goblet, ] %>%
dcast2(Pathway~L1,
fun.aggregate = sum, value.var = "value")
pmat_Goblets_Macrophage <- pmat_Goblets_Macrophage[rowSums(pmat_Goblets_Macrophage) != 0 &
rowSums(pmat_Goblets_Macrophage != 0) > 2, ]
pmat_Goblets_Macrophage <- pmat_Goblets_Macrophage[, -1]
hclust_pathway <- hclust(dist((-1/log(pmat_Goblets_Macrophage))),
method = "ward.D2")
nclust_pathway = 4
pathway_clust <- cutree(hclust_pathway, k = nclust_pathway)
anno_row <- data.frame(pathway_cluster = factor(pathway_clust))
rownames(anno_row) <- names(pathway_clust)
anno_color$pathway_cluster <- RColorBrewer::brewer.pal(nclust_pathway, "Set2")
names(anno_color$pathway_cluster) <- seq_len(nclust_pathway)
pheatmap(-1/log(pmat_Goblets_Macrophage),
annotation_col = anno_col[, 1, drop = FALSE],
annotation_colors = anno_color,
annotation_row = anno_row,
clustering_method = "ward.D2",
color = colorRampPalette(c("white",
brewer.pal(n = 9, name = "Reds")))(100),
main = "Ligand: Goblets; Receptor: Macrophages, Monocyte, T",
#file = "figures/ChuaEtAl/cellchat_LigandGoblet_ReceptorImmune_heatmap_new.pdf",
height = 12,
width = 10
)
dev.off()
## null device
## 1
Comparison across the same sample
rankNet_ratio <- function(sample1_name, sample2_name,
ligand, receptor) {
BIH_sample1 <- rankNet_byGroup_agg %>%
filter(sample %in% sample1_name)
BIH_sample2 <- rankNet_byGroup_agg %>%
filter(sample %in% sample2_name)
BIH <- merge(BIH_sample1, BIH_sample2,
by = colnames(BIH_sample1)[!colnames(BIH_sample1) %in% c("value", "sample")])
BIH <- BIH[BIH$value.y != 0 | BIH$value.x != 0, ]
BIH_subset <- BIH %>% filter(Ligand_group %in% ligand,
Receptor_group %in% receptor)
BIH_subset$ratio <- (-1/log(BIH_subset$value.y)) / (-1/log(BIH_subset$value.x + min(BIH_subset$value.x[BIH_subset$value.x != 0]) * 0.1))
BIH_subset$Pathway <- factor(BIH_subset$Pathway,
levels = unique(BIH_subset$Pathway[order(BIH_subset$ratio)]))
return(BIH_subset)
}
BIH_6_subset <- rankNet_ratio("BIH-CoV-06_NS_1",
"BIH-CoV-06_NS_2",
"Monocyte",
"Neutrophil")
BIH_7_subset <- rankNet_ratio("BIH-CoV-07_NS_1",
"BIH-CoV-07_NS_2",
"Monocyte",
"Neutrophil")
BIH_12_subset <- rankNet_ratio("BIH-CoV-12_NS_1",
"BIH-CoV-12_NS_2",
"Monocyte",
"Neutrophil")
BIH_15_subset <- rankNet_ratio("BIH-CoV-15_NS_1",
"BIH-CoV-15_NS_2",
"Monocyte",
"Neutrophil")
BIH_subset <- rbind(BIH_6_subset,
BIH_7_subset,
BIH_12_subset,
BIH_15_subset)
BIH_subset$sample <- gsub("_NS_1", "", BIH_subset$sample.x)
tab <- table(BIH_subset[log(BIH_subset$ratio) > 0.01, ]$Pathway)
BIH_subset <- BIH_subset %>% filter(Pathway %in% names(tab[tab > 1]))
BIH_subset$ratio[BIH_subset$ratio == 0] <- NA
ggplot(BIH_subset,
aes(x = Pathway,
y = log(ratio),
fill = sample)) +
geom_col(position = "dodge", width = 0.7) +
theme_bw() +
theme(aspect.ratio = 0.5,
axis.text.x = element_text(angle = 90,
hjust = 1)) +
scale_fill_manual(values = tableau_color_pal("Tableau 20")(20)[c(11:12, 3:4)]) +
#coord_flip() +
# facet_wrap(~Pathway) +
ggtitle("Ligand: Monocyte; Receptor: Neutrophil") +
NULL
ggsaveWithDate("figures/ChuaEtAl/CCI_TimelineComparison_logRatio_MonocyteNeutrophil.pdf",
width = 8, height = 5)
BIH_6_subset <- rankNet_ratio("BIH-CoV-06_NS_1",
"BIH-CoV-06_NS_2",
"Goblet",
c("Monocyte", "Macrophage", "T"))
BIH_6_subset_agg <- BIH_6_subset %>% group_by(Pathway) %>%
mutate(value.x = sum(value.x),
value.y = sum(value.y)) %>%
distinct(Pathway, .keep_all = T) %>%
data.frame()
BIH_6_subset_agg$ratio <- (-1/log(BIH_6_subset_agg$value.y)) / (-1/log(BIH_6_subset_agg$value.x + min(BIH_6_subset_agg$value.x[BIH_6_subset_agg$value.x != 0]) * 0.1))
BIH_6_subset_agg$Pathway <- factor(BIH_6_subset_agg$Pathway,
levels = unique(BIH_6_subset_agg$Pathway[order(BIH_6_subset_agg$ratio)]))
BIH_7_subset <- rankNet_ratio("BIH-CoV-07_NS_1",
"BIH-CoV-07_NS_2",
"Goblet",
c("Monocyte", "Macrophage", "T"))
BIH_7_subset_agg <- BIH_7_subset %>% group_by(Pathway) %>%
mutate(value.x = sum(value.x),
value.y = sum(value.y)) %>%
distinct(Pathway, .keep_all = T) %>%
data.frame()
BIH_7_subset_agg$ratio <- (-1/log(BIH_7_subset_agg$value.y)) / (-1/log(BIH_7_subset_agg$value.x + min(BIH_7_subset_agg$value.x[BIH_7_subset_agg$value.x != 0]) * 0.1))
BIH_7_subset_agg$Pathway <- factor(BIH_7_subset_agg$Pathway,
levels = unique(BIH_7_subset_agg$Pathway[order(BIH_7_subset_agg$ratio)]))
BIH_12_subset <- rankNet_ratio("BIH-CoV-12_NS_1",
"BIH-CoV-12_NS_2",
"Goblet",
c("Monocyte", "Macrophage", "T"))
BIH_12_subset_agg <- BIH_12_subset %>% group_by(Pathway) %>%
mutate(value.x = sum(value.x),
value.y = sum(value.y)) %>%
distinct(Pathway, .keep_all = T) %>%
data.frame()
BIH_12_subset_agg$ratio <- (-1/log(BIH_12_subset_agg$value.y)) / (-1/log(BIH_12_subset_agg$value.x + min(BIH_12_subset_agg$value.x[BIH_12_subset_agg$value.x != 0]) * 0.1))
BIH_12_subset_agg$Pathway <- factor(BIH_12_subset_agg$Pathway,
levels = unique(BIH_12_subset_agg$Pathway[order(BIH_12_subset_agg$ratio)]))
BIH_15_subset <- rankNet_ratio("BIH-CoV-15_NS_1",
"BIH-CoV-15_NS_2",
"Goblet",
c("Monocyte", "Macrophage", "T"))
BIH_15_subset_agg <- BIH_15_subset %>% group_by(Pathway) %>%
mutate(value.x = sum(value.x),
value.y = sum(value.y)) %>%
distinct(Pathway, .keep_all = T) %>%
data.frame()
BIH_15_subset_agg$ratio <- (-1/log(BIH_15_subset_agg$value.y)) / (-1/log(BIH_15_subset_agg$value.x + min(BIH_15_subset_agg$value.x[BIH_15_subset_agg$value.x != 0]) * 0.1))
BIH_15_subset_agg$Pathway <- factor(BIH_15_subset_agg$Pathway,
levels = unique(BIH_15_subset_agg$Pathway[order(BIH_15_subset_agg$ratio)]))
BIH_subset <- rbind(BIH_6_subset_agg,
BIH_7_subset_agg,
BIH_12_subset_agg,
BIH_15_subset_agg)
BIH_subset$sample <- gsub("_NS_1", "", BIH_subset$sample.x)
tab <- table(BIH_subset[log(BIH_subset$ratio) > 0.01, ]$Pathway)
BIH_subset <- BIH_subset %>% filter(Pathway %in% names(tab[tab > 1]))
BIH_subset$ratio[BIH_subset$ratio == 0] <- NA
ggplot(BIH_subset,
aes(x = Pathway,
y = log(ratio),
fill = sample)) +
geom_col(position = "dodge", width = 0.7) +
theme_bw() +
theme(aspect.ratio = 0.5,
axis.text.x = element_text(angle = 90,
hjust = 1)) +
scale_fill_manual(values = tableau_color_pal("Tableau 20")(20)[c(11:12, 3:4)]) +
#coord_flip() +
# facet_wrap(~Pathway) +
ggtitle("Ligand: Goblet; Receptor: T, Macrophage, Monocyte") +
NULL
ggsaveWithDate("figures/ChuaEtAl/CCI_TimelineComparison_logRatio_GobletImmune.pdf",
width = 8, height = 5)
Session Info
sessionInfo()
## R version 4.0.2 RC (2020-06-20 r78727)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggraph_2.0.3 igraph_1.1.0
## [3] ggrepel_0.8.2 scales_1.1.1
## [5] gridExtra_2.3 RColorBrewer_1.1-2
## [7] cluster_2.1.0 CellChat_0.0.1
## [9] bigmemory_4.5.36 pheatmap_1.0.12
## [11] stringr_1.4.0 dplyr_1.0.2
## [13] reshape2_1.4.4 ggpubr_0.3.0
## [15] ggthemes_4.2.0 moon_0.1.0
## [17] scattermore_0.6 scater_1.16.1
## [19] ggplot2_3.3.2 scran_1.16.0
## [21] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
## [23] DelayedArray_0.14.0 matrixStats_0.56.0
## [25] Biobase_2.48.0 GenomicRanges_1.40.0
## [27] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [29] S4Vectors_0.26.1 BiocGenerics_0.34.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8
## [3] circlize_0.4.10 systemfonts_0.2.3
## [5] NMF_0.30.1 plyr_1.8.6
## [7] BiocParallel_1.22.0 listenv_0.8.0
## [9] gridBase_0.4-7 digest_0.6.25
## [11] foreach_1.5.0 htmltools_0.5.0
## [13] viridis_0.5.1 ggalluvial_0.12.0
## [15] magrittr_1.5 doParallel_1.0.15
## [17] openxlsx_4.1.5 limma_3.44.3
## [19] graphlayouts_0.7.0 sna_2.5
## [21] ComplexHeatmap_2.4.2 globals_0.12.5
## [23] svglite_1.2.3.2 colorspace_1.4-1
## [25] haven_2.3.1 xfun_0.18
## [27] crayon_1.3.4 RCurl_1.98-1.2
## [29] jsonlite_1.6.1 bigmemory.sri_0.1.3
## [31] iterators_1.0.12 glue_1.4.1
## [33] polyclip_1.10-0 registry_0.5-1
## [35] gtable_0.3.0 zlibbioc_1.34.0
## [37] XVector_0.28.0 GetoptLong_1.0.0
## [39] car_3.0-8 BiocSingular_1.4.0
## [41] future.apply_1.5.0 shape_1.4.4
## [43] abind_1.4-5 edgeR_3.30.3
## [45] rngtools_1.5 bibtex_0.4.2.2
## [47] rstatix_0.6.0 Rcpp_1.0.4.6
## [49] viridisLite_0.3.0 xtable_1.8-4
## [51] clue_0.3-57 reticulate_1.16
## [53] dqrng_0.2.1 foreign_0.8-80
## [55] rsvd_1.0.3 FNN_1.1.3
## [57] ellipsis_0.3.1 farver_2.0.3
## [59] pkgconfig_2.0.3 locfit_1.5-9.4
## [61] labeling_0.3 tidyselect_1.1.0
## [63] rlang_0.4.9 munsell_0.5.0
## [65] cellranger_1.1.0 tools_4.0.2
## [67] generics_0.0.2 statnet.common_4.3.0
## [69] broom_0.7.2 evaluate_0.14
## [71] yaml_2.2.1 knitr_1.30
## [73] tidygraph_1.2.0 zip_2.0.4
## [75] purrr_0.3.4 dendextend_1.13.4
## [77] pbapply_1.4-2 future_1.17.0
## [79] compiler_4.0.2 beeswarm_0.2.3
## [81] curl_4.3 png_0.1-7
## [83] ggsignif_0.6.0 tweenr_1.0.1
## [85] tibble_3.0.4 statmod_1.4.34
## [87] stringi_1.4.6 RSpectra_0.16-0
## [89] gdtools_0.2.2 forcats_0.5.0
## [91] lattice_0.20-41 Matrix_1.2-18
## [93] vctrs_0.3.5 pillar_1.4.4
## [95] lifecycle_0.2.0 GlobalOptions_0.1.2
## [97] BiocNeighbors_1.6.0 data.table_1.12.8
## [99] cowplot_1.0.0 bitops_1.0-6
## [101] irlba_2.3.3 R6_2.4.1
## [103] network_1.16.0 rio_0.5.16
## [105] vipor_0.4.5 codetools_0.2-16
## [107] MASS_7.3-51.6 assertthat_0.2.1
## [109] pkgmaker_0.31.1 rjson_0.2.20
## [111] withr_2.2.0 GenomeInfoDbData_1.2.3
## [113] hms_0.5.3 grid_4.0.2
## [115] coda_0.19-3 tidyr_1.1.2
## [117] rmarkdown_2.4 DelayedMatrixStats_1.10.0
## [119] carData_3.0-4 ggforce_0.3.1
## [121] ggbeeswarm_0.6.0